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yt-channel-intel

yt-channel-intel is a skill for Claude Code from not0lucky/tubescout. It costs 82 tokens per session (596 once invoked), scanned A, original, MIT.

A YouTube channel analysis skill that compares what a channel publishes with which videos perform best. It examines publishing frequency, positioning, titles, engagement, and—when requested—video transcripts.

In plain words
What is it for?
Use it to analyze a channel, explain why it works, study competitor content, or develop content ideas based on its strongest and unusual-performing videos.
Why use it?
It helps separate the channel's stated topic from the subjects and presentation styles that its audience actually responds to.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin. Also seen: mentions Codex.

Part of the tubescout plugin — 6 skills, 1 MCP server shipped together

not rated 0repo 16d ago A scan 82 tokens original MIT

Good fit Use it to analyze a channel, explain why it works, study competitor content, or develop content ideas based on its strongest and unusual-performing videos.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/not0lucky/tubescout/yt-channel-intel
View source ↗ not0lucky/tubescout
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add not0lucky/tubescout --skill yt-channel-intel
Clone the repo
git clone --depth 1 https://github.com/not0lucky/tubescout

Made for: Claude Code.

Or install tubescout, the plugin that ships this one along with the rest of its 6 skills, 1 MCP server.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for yt-channel-intel

README.md
[![agentmods](https://agentmods.dev/badge/skills/not0lucky/tubescout/yt-channel-intel/github.svg)](https://agentmods.dev/skills/not0lucky/tubescout/yt-channel-intel)
Your own site
<a href="https://agentmods.dev/skills/not0lucky/tubescout/yt-channel-intel"><img src="https://agentmods.dev/badge/skills/not0lucky/tubescout/yt-channel-intel/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for yt-channel-intel

Your own site · 80×15
<a href="https://agentmods.dev/skills/not0lucky/tubescout/yt-channel-intel"><img src="https://agentmods.dev/badge/skills/not0lucky/tubescout/yt-channel-intel.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 82 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 596 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00082 $0.00596
Opus 5 $0.00041 $0.00298
Sonnet 5 $0.00016 $0.00119
Haiku 4.5 $0.00008 $0.00060

Measured 12d ago against content hash 139118723363, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

yt-channel-intel scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/yt-channel-intel/SKILL.md · 41 lines

How it starts

The opening of the file, as written. The whole thing — 41 lines — stays where its author put it; the contents beside it link to each section on GitHub.

yt-channel-intel

Channels reveal their strategy in the gap between what they publish and what performs.

Process

  1. Scan. get_channel_videos with maxVideos 30–60. Note subscriber count, description/positioning, and upload cadence (cluster the published fields).
  2. Find the outliers. Views per video vs the channel's median. Outliers (>3× median) are the audience voting; they matter more than the channel's stated focus. get_video on the top 2–3 outliers for engagement detail and keywords.
  3. Read the packaging. Across titles: the repeated formats ("How I…", "$X/month…", numbers, brackets), title length, what the thumbnails' promises have in common (infer from titles/durations — don't fetch images).
  4. Optional depth. If the user wants the content strategy, get_transcript on one median video + one outlier and compare structure (hook, pacing, CTA placement).
  5. Report. Positioning in one line → cadence → what performs vs what they make → the 3 transferable tactics, each tied to the specific videos that prove it.

Rules

  • Cite only URLs returned by tubescout tools in this conversation — never write a YouTube URL or video ID from memory.
  • Views are cumulative — normalize by age when comparing recent vs old uploads ("3 days ago, 30K" can beat "2 years ago, 100K").
  • Subscriber count is vanity; views-per-video and outlier ratio are the signal.
  • If the user runs a channel in the same niche, end with the gap analysis: demand the target channel proved that the user's channel isn't serving yet.
  • Use the conversation's context: if the user's own content, product, or niche came up earlier in the chat, frame every transferable tactic as a concrete move for THEM ("your X could…"), not as abstract channel advice.

Troubleshooting

  • Tools missing → connect tubescout: claude mcp add --scope user tubescout -- npx -y tubescout or codex mcp add tubescout -- npx -y tubescout.
  • Channel resolves but 0 videos → it may be a Shorts-only or live-only channel; say so rather than reporting "inactive".

Read the full file on GitHub · 41 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 12d ago First seen · 41 lines · 82 tokens per session scan A 139118723363

Subscribe to this mod's changes

yt-channel-intel is a skill published in the GitHub repository not0lucky/tubescout (0 stars, last pushed 16d ago), licensed MIT. It adds 82 tokens to every session and 596 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

Analytics and metricsMarketing and SEOclaudecodexmcpmodel-context-protocoltranscript
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